Skip to main content
SpringerLink
Log in

On the Analytical Postprocessing of Technical and Economic Information

  • GENERAL SECTION
  • Published:
Automatic Documentation and Mathematical Linguistics Aims and scope

Abstract—Topical aspects of modern computer science in their relation to the issues of analysis of technical and economic information and econometric research have been investigated. The feasibility of abandoning the methods of classical statistics and applying robust and nonparametric data processing procedures in real conditions of the heterogeneity of observations and significant deviations of empirical distributions from the normal law is justified. The possibilities and areas of application of econometric models, methods of nonparametric statistics, science metrics, and multidimensional data analysis, for production of information-analytical products and services are shown. The prospects of analytical postprocessing of techno-economic information in the structure of big data technologies are considered. The purpose of this article was to show the potential and systemic role of analytical post-processing of technical and economic information in the formation of a new digital information environment.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

REFERENCES

  1. Borisova, L.F. and Syuntyurenko, O.V., VINITI RAN abstract database: Prospects of information postprocessing using methods of data analysis, Sci. Tech. Inf. Process., 2007, vol. 34, pp. 278–283.

    Article  Google Scholar 

  2. Rodionov, I.I., Gilyarevskii, R.S., and Tsvetkova, V.A., Informatsionnaya deyatel’nost' kak infrastruktura natsional’noi ekonomiki (Information Activity as an Infrastructure of the National Economy), St. Petersburg: Aleteya, 2016.

  3. Syuntyurenko, O.V. and Gilyarevskii, R.S., The use of scientometric methods and comparative data analysis for the management of scientific research in thematic areas, Nauchno-Tekh. Inf., Ser. 2, 2016, no. 12, pp. 1–12.

  4. Dmitrieva, E.Yu. and Syuntyurenko, O.V., Tasks of information support of import substitution and conversion, Inf. Innovatsii, 2020, no. 3, pp. 14–20.

  5. Wiener, N., Tvorets i robot (Creator and Robot), Moscow: Progress, 1966.

  6. Mosteller, F. and Tukey, J., Data Analysis and Regression, Addison-Wesley Publ. Co., 1977.

    Google Scholar 

  7. Tukey, J., Data analysis, computing, and mathematics, in Sovremennye problemy matematiki (Current Problems of Mathematics), Moscow: Znanie, 1977.

  8. Artamonov, G.T., Kondrat’ev, I.A., and Cherepanov, E.V., On the correctness of the analysis of factual data by statistical methods, Autom. Doc. Math. Linguist., 1981, no. 7, pp. 17–23.

  9. Tarasenko, F.P. and Cherepanov, E.V., Analysis of distributions of technical and economic data on the basis of nonparametric polygram estimates, Tekh. Sredstv Svyazi, Ser.: Tekh. Ekon. Upr., 1986, no. 2, pp. 76–87.

  10. Cherepanov, E.V., Automated analysis of statistical tables of technical and economic data, Sist. Sredstva Svyazi Televideniya Radioveshchaniya, Nauchno-Tekh. Zh., 2011, nos. 1–2, pp. 18–22.

  11. Syuntyurenko, O.V., Theoretical and applied aspects of automating multivariate analysis procedures, Autom. Doc. Math. Linguist., 2018, vol. 52, no. 6, pp. 275–281.

    Article  Google Scholar 

  12. Tukey, J.W., Exploratory Data Analysis, Addison-Wesley Publ. Co., 1977.

    MATH  Google Scholar 

  13. Tyurin, Yu.N. and Shmerling, D.S., Nonparametric methods of statistics, Sotsiol.: Metodol. Metody Mat. Mod., 2004, no. 18, pp. 154–166.

  14. Kobzar’, A.I., Prikladnaya matematicheskaya statistika (Applied Mathematical Statistics), Moscow: Fizmatlit, 2006.

  15. Johnston, J., Econometric Methods, McGraw-Hill, 1971.

    Google Scholar 

  16. Syuntyurenko, O.V. and Cherepanov, E.V., Informatics: Data analysis and econometrics, Sredstva Svyazi, 1986, no. 4, pp. 116–124.

  17. Classical Methods of Statistics: Student’s t-test. https://r-analytics.blogspot.com/2012/03/t.html. Accessed February 18, 2021.

  18. Barnabas K. Tannahill and Mo Jamshidi, System of systems and big data analytics—bridging the gap, Comput. Electr. Eng., 2014, vol. 40, no. 1, pp. 2–15. https://www.sciencedirect.com/science/article/abs/pii/S004579061300298X.

    Article  Google Scholar 

  19. Krivenko, M.P., Reconstruction of axes of principal components, Inf. Ee Primen., 2018, vol. 12, no. 1, pp. 71–77.

    Google Scholar 

  20. Rodriguez-Mazahua, L., et al., A general perspective of Big Data: Applications, tools, challenges and trends, J. Supercomput., 2016, vol. 72, pp. 3073–3113.

    Article  Google Scholar 

Download references

Funding

This article was supported by the Russian Foundation for Basic Research (project no. 20-07-00014).

Author information

Authors and Affiliations

  1. All-Russian Institute for Scientific and Technical Information (VINITI), Russian Academy of Sciences, 125315, Moscow, Russia

    O. V. Syunturenko

Authors
  1. O. V. Syunturenko
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to O. V. Syunturenko.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Syunturenko, O.V. On the Analytical Postprocessing of Technical and Economic Information. Autom. Doc. Math. Linguist. 55, 135–139 (2021). https://doi.org/10.3103/S0005105521040038

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S0005105521040038

Keywords:

Search

Navigation